Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.|Advances in computational omics technologies are enabling access to the hidden diversity of natural products, and artificial intelligence approaches are facilitating key steps in harnessing the therapeutic potential of such compounds, including biological activity prediction. This article discusses synergies between these fields to effectively identify drug candidates from the plethora of molecules produced by nature, and how to address the challenges in realizing the potential of these synergies.
Title
Artificial intelligence for natural product drug discovery
Published in
Nature Reviews Drug Discovery
Date
2023-09-11
Publisher
Nature Portfolio, Berlin
ISSN
1474-1776
1474-1784
Grant
All authors thank the Lorentz Center and Leiden University for funding the Lorentz Workshop 'Artificial Intelligence for Natural Product Drug Discovery' that laid the foundation for this Review. M.W.M. was supported by funds from the Duchossois Family Inst
Leiden University
Duchossois Family Institute at the University of Chicago: BB/R022054/1
UK Research and Innovation Biotechnology and Biological Sciences Research Council: 2047235
NSF CAREER award: ASDI.2017.030
ASDI eScience grant from the Netherlands eScience Center: 725523
European Research Council: NNF20CC0035580
Novo Nordisk Foundation
LOEWE Center for Translational Biodiversity Genomics
Chemical Industry Germany: 239748522
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): 15-CE29-0001
National French Agency (ANR)
National Library of Medicine training grant: NLM 5T15LM007359
Computation and Informatics in Biology and Medicine Training Program
Klaus Faber Foundation: NRF 2018R1A5A2023127
National Research Foundation of Korea (NRF) - Korean government (MSIT): 2022R1F1A107462311
NRF: G061821N
Research Foundation - Flanders: DBI-1845890
US National Science Foundation: 2021-FLG-3819
NC Biotech: NIH NIDDK DK034987
UNC CGIBD Pilot Award
Duke Cancer Institute: NIH NCI CA014236
Duke Microbiome Center Pilot Award: EEC-2133504
Engineering Research Center for Precision Microbiome Engineering (NSF)
Duke Science and Technology Initiative: 180544
NCCR Catalysis
National Centre of Competence in Research - Swiss National Science Foundation: 2124-390838134
Germany's Excellence Strategy - EXC
KAIST Key Research Institute (Interdisciplinary Research Group) Project: U41-AT008718
US NIH
Eawag discretionary funding: DECIPHER-948770
ERC
Record creation date
2024-02-19